→ Batch and streaming pipelines using Airflow, dbt, Spark, Kafka, Fivetran, and Snowflake
→ Data lakes and lakehouses, serverless compute, automated scaling, and storage optimization
→ Automated API integrations, schema mapping, and legacy-to-cloud migrations
→ Lineage tracking, anomaly detection, data cataloging, and continuous validation
→ Feature store development, real-time model input streams, and versioned datasets for MLOps
Solution: Built a no-code data platform with automated importing, transformation, and forecasting.
Outcome:
80% reduction in FP&A setup time
Unified reporting across multiple data sources
Onboarded real customers within weeks of MVP launch
Result: A scalable financial data platform running fully automated pipelines across AWS Aurora, Lambda, and S3.
Challenge: Keller Williams needed to unify massive CRM data for 200K+ agents, automate marketing campaigns, and manage lead data at scale.
Solution: Designed a microservices-based data architecture for seamless CRM integration and campaign tracking.
Outcome:
High adoption across 200K users in days
Campaign creation time cut by over 60%
Real-time lead scoring and data sync across Google Cloud
Result: Enabled $400K+ transaction handling per campaign with enterprise-grade scalability and reliability.
Solution: Built real-time data pipelines and AWS-based infrastructure for rental, transaction, and user data management.
Outcome:
$24.1M+ in transaction volume within a year
70% applicant conversion rate
Auto-scaling data architecture that powers 500+ active units and 533 renters
Result: A unified data backbone enabling automation, analytics, and business growth across web and mobile.
Let Logiciel’s Data Engineering Team help you design the pipelines, infrastructure, and governance that scale with your ambitions.
